Gear Grinding Research Articles

Numerical investigation on tooth surface waviness in continuous generating grinding of electric vehicle gears considering the main shaft vibration and grinding worm wear

The objective of this paper is to present a numerical approach to investigate the formation mechanism of tooth surface waviness in CGG of electric vehicle gears while independently analyzing each influencing factor and ignoring the mutual influences between them. A tooth surface waviness simulation model is proposed considering the system vibration and tool wear. According to the characterization of grinding worm wear and the main shaft vibration in CGG, the grinding worm wheel and the grinding trajectories are modeled. Based on the analysis of the geometric contact characterizations through the whole grinding process, tooth surface topography is modeled, and tooth surface waviness is extracted. By comparing the tooth surface waviness in the frequency domain, the influence of grinding worm local wear, global wear, and the main shaft vibrations on tooth surface waviness is studied. Local wear and global wear affect tooth surface waviness differently by changing the amplitude and the distribution pattern of the frequencies. The main shaft vibrations have limited direct impacts on the amplitude in profile and flank spectrums, among which the vibration Z plays a leading role.

An improved robust identification method for position independent geometric errors of the swing axis of the gear grinding machine

An improved robust identification method for position independent geometric errors of the swing axis of the gear grinding machine

High-Speed and Low-Noise Gear Finishing by Gear Grinding and Honing: A Review

AbstractGears are important mechanical parts for transmitting power and play an increasingly important role in the machinery industry. The electric vehicle industry developed rapidly and became a vital development field of the automotive industry over the past few years. The conversion of energy increases the requirements for electric vehicle transmissions, which promotes the development of high-speed and low-noise transmission gear. This paper elaborates on the research progress of high-speed and low-noise gear finishing methods. Firstly, this review analyses the machining requirements, finishing, and specific machining technologies of high-speed and low-noise gears. The importance of gear grinding and honing in high-speed and low-noise gear machining is highlighted. Secondly, the applications of gear grinding and honing in gear modification, error compensation, and texture control are analyzed. Furthermore, the role of precision machining technology in improving gear performance is clarified. Finally, the design and processing methods of the modified tooth flank for gear modification are summarized. In addition, the influence of tooth surface texture on noise is described, and the texture change methods are explored. The well-known open problems of gear finishing are finally identified, and some new research interests are also pointed out. This review will provide valuable references for further research on gear finishing.

Feed shaft thermal expansion error modeling based on principal component regression

In order to further improve the prediction accuracy of the thermal error model of the feed shaft of the gear grinding machine, this paper proposes a feed shaft thermal expansion error modeling method based on principal component regression. The feed shaft positioning error is decoupled by linear fitting to obtain the thermal expansion slope parameter, the position correlation of the feed shaft thermal expansion error is eliminated, and a regression model of the thermal expansion slope parameter and the temperature of all measuring points is established based on the principal component regression algorithm. Different from the traditional method, the principal component regression model does not require additional screening of temperature sensitive points, and the root mean square error mean and standard deviation of the group experimental prediction results can reach 2.0 μm/m、0.9μm/m, which has higher accuracy and stability than conventional methods.

Geometric Parameter Optimization for Axial Modification in Helical Gear Form Grinding

During the axial modification in helical gear form grinding, the contact line between the grinding wheel and the gear constantly changes, and the additional radial motion can cause a “tooth surface twist” phenomenon. An optimization method for tooth surface twist error was proposed to address this. Based on the gear meshing principles, a mathematical model for axial modification in form grinding was established to solve for the instantaneous contact lines at various positions on the actual modified tooth surface. By analyzing the influence of the grinding wheel installation angle on the axial modification contact line, an optimization model was constructed to reduce the twist of the transverse profile, reduce the twist of the flank profile, reduce the helix deviation, and improve the form grinding efficiency. The practical implications of this research are significant, as the Particle Swarm Optimization (PSO) algorithm was employed to optimize the form grinding parameters, leading to a method that effectively reduced tooth surface twist error and improved the form grinding accuracy of the modified tooth surface.

A Study on the Analysis of Machining Characteristics and Derivation of Optimal Machining Conditions of Grinding Wheel for Gear Grinding

A Study on the Analysis of Machining Characteristics and Derivation of Optimal Machining Conditions of Grinding Wheel for Gear Grinding

Investigation of Dimensional and Shape Changes in Combined Surface Layer Heat Treatments of Gear Components Made of EN31CrMoV9 and EN42CrMo4

Abstract Surface heat treatments such as case hardening, nitriding and surface hardening are often used to improve the performance properties of gear components. Due to limitations in the individual processes, e. g., with regard to the distortion that occurs, the achievable hardening depth or surface hardness, combinations of several processes are important in order to achieve better process efficiency and component properties. The present investigations focus on dimensional and shape changes in the combination of nitriding and induction hardening on samples and gears made of the materials EN31CrMoV9 and EN42CrMo4, measured before and after the heat treatments. For the gears examined, comparable dimensional and shape changes were determined after the combination treatment as for induction hardening alone, which overall resulted in larger geometric changes than nitriding. Due to the dimensional and shape deviations of the geometric features, reworking by means of gear grinding is advisable in certain cases.

The Grinding and Correction of Face Gears Based on an Internal Gear Grinding Machine

This paper presents a method of calculating and correcting grinding face gears on an internal gear grinding machine. The generating principle of face gears is studied, and the feasibility of grinding motion on an internal gear grinding machine is analyzed. Then, the motions that need to be followed for grinding are analyzed based on the gear machine tool structure. Four main error sources causing tooth surface deviation in the grinding movements are proposed. The mathematical modeling of the grinding of face gears containing proposed error sources on an internal gear grinding machine is accurately established. The influence of the error sources on the topological deviations of the tooth surface is explored. A sensitivity matrix is established for the influence of various error factors on the tooth surface deviations. The correction values of each error factor are obtained in the case of existing tooth surface deviations. Finally, a virtual machining experiment is conducted, which proves the accuracy of the proposed method for characterizing grinding and realizing corrections.

Operational State Recognition of Continuous Worm Gear Grinding Process Based on Wavelet Packet Energy Ratio and Double-Layer SVM Classifier

Operational State Recognition of Continuous Worm Gear Grinding Process Based on Wavelet Packet Energy Ratio and Double-Layer SVM Classifier

Improving the efficiency of gear grinding technology for mine conveyor drives

The aim of this work is to develop a mathematical model of the gear grinding process using the method of high-performance profile copying and determine the optimal parameters of grinding modes according to the temperature criterion. It is shown that in order to ensure defect-free machining of gears under deep grinding conditions, it is necessary to significantly reduce the rate of the feed speed of the grinding wheel along the tooth of the gear wheel. For a grinding depth of 0.4 mm, it should be equal to 2.27 m/min. In this case, the machining productivity can be increased up to 5 times with the simultaneous elimination of the formation of sticking, microcracks and other temperature defects on the machined gear surfaces. This processing effect is achieved by using a modern HOFLER RAPID 1250 gear grinding machine and highly porous abrasive wheels, which are characterized by high cutting capacity. Thanks to the improved quality of gear processing, the quality and performance of manufactured mine conveyor drives (planetary gearboxes with a power of more than 200 kW) have been improved, and the number of repairs required in harsh mine conditions has been significantly reduced.

Research on the calculation method of grinding temperature field of spiral bevel gear based on generative machining

At present, the process parameter setting of spiral bevel gear grinding is mainly based on an empirical method, and the tooth surface temperature is difficult to estimate effectively during grinding, which may lead to excessive tooth surface temperature and surface burn caused by improper selection of process parameters. Aiming at the above problems, this paper presents a method for calculating the temperature field of spiral bevel gear forming based on a combination of analytical and numerical methods. The motion equation of spiral bevel gear grinding is derived based on the method of tooth surface development. The time-varying heat flow calculation method in tooth surface grinding was developed through the discrete and two-dimensional contact zone. The time-varying temperature field analysis method of spiral bevel gear forming is developed using the time-varying heat flow and finite element calculation method. The related research can realize the temperature prediction of grinding tooth surfaces and provide the basis for improving grinding technology.

Residual stresses in gear form grinding by considering carburizing heat treatment: multi-field coupled numerical simulation and experimental verification

Residual stresses in gear form grinding by considering carburizing heat treatment: multi-field coupled numerical simulation and experimental verification

Effect of grinding parameters on tooth surface waviness in continuous generating grinding for electric vehicle gears

Compared with traditional fuel vehicle gears, electric vehicle (EV) gears in its transmission system suffer harsher working conditions with stricter quality demands. Especially, regarding the need for low noise, as an indispensable acceptance indicator in the EV gear manufacturing industry, tooth surface waviness of EV gears has received great attention. However, in continuous generating grinding (CGG), many factors affect tooth surface waviness and tooth surface waviness is still highly unpredictable. Therefore, aiming at improving the gear noise of EVs, the effect of grinding parameters (grinding speed, feed rate, and normal stock) on tooth surface waviness is clarified with other interferences eliminated. Considering the characterization of both CGG and tooth surface geometry, a tooth surface evaluation approach for a single tooth surface was proposed to characterize the meso-scale surface geometric unevenness and directly reflect the processing quality. A tooth surface waviness model was proposed to study the effect of grinding parameters on tooth surface waviness. In the model, the spatiotemporal variability characteristics in CGG are considered while ignoring system vibration, machine tool errors, and so on. By simulating the evolution process of the remaining flank stock, tooth surface topography is modeled, from which simulated tooth surface waviness is extracted. To verify the proposed tooth surface waviness model, the gear grinding experiments were conducted. Under different grinding parameters, the simulated tooth surface waviness is consistent with the experimental results in the length-domain and frequency-domain. Furthermore, it was found that grinding parameters change the flank mean value, flank peak-to-peak value, and profile mean value in length-domain, and influence the distribution pattern and amplitude of the spectrums in the frequency-domain. Profile tooth surface waviness was found to be influenced by grinding parameters irregularly and flank tooth surface waviness was found to be more susceptible to the change of normal stock in the length-domain and frequency-domain.

Grinding Away

The arrival of e-mobility has heightened the demands for precision and process reliability in automotive engineering. Against this backdrop, Emag-Su offers a range of solutions for gear grinding.

Prediction of surface topography in face gear grinding based on dynamic contour interferometric sampling method

Prediction of surface topography in face gear grinding based on dynamic contour interferometric sampling method

Simulation of Continuous Generating Gear Grinding Based on Numerical Calculation

Continuous generating grinding (CGG) is a widely utilized finishing process in the mass production of gears. However, due to the high grinding speed and material removal rate, a significant amount of heat is generated during the process. To optimize this process, the development of a highly accurate simulation shows great promise. The simulation involves three key steps: kinematic analysis, removal analysis, and performance analysis. By calculating and comparing surface temperature and grinding resistance with data obtained from a CGG experiment conducted under identical grinding conditions, it is evident that the simulation exhibits a commendable level of accuracy.

A gear grinding allowance stochastic simulation model

The paper proposes a method and technique that allow, using a minimum sample (3 units) from a batch (30 units) of real gears, to obtain stochastic information about the actual distribution of the gear grinding allowance both on the left and right flanks of the gear gaps for all gears in the batch. The proposed gear grinding allowance stochastic model is based on the representation of the distribution of the gear grinding allowance along the gear periphery as a superposition of a sinusoidal component with a random amplitude and a random component of the white noise type. The conditions for the equivalence of the stochastic characteristics of real (measured) and virtual (not actually measured) gears are formulated, namely the ratio between the amplitude of the sinusoidal component and the average amplitude of the high-frequency harmonic components of the gear grinding allowance distribution along the gear periphery. The gear grinding allowance stochastic model adjusted according to the proposed method makes it possible to predict the distribution of the gear grinding allowance on the left and right flanks of the gear gaps for a batch of gears and, taking into account the information obtained (replacing the experimental data), both evaluate the existing technology and optimize the gear grinding allowance value. As a result, it becomes possible to assess the quality of the technological process of manufacturing the gear, taking into account the effect random deformations to gear grinding allowance at the stage of their heat treatment (hardening), which is performed before the gear grinding operation. In turn, optimization of the grinding allowance (based on its actual distribution along the periphery of the gear) makes it possible to reduce both defects in burns (with increased allowance) and defects in unground teeth (with insufficient allowance).

Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.

Online monitoring method of non-cylindrical wheel wear for gear grinding based on dynamic force model

Traditional monitoring methods of grinding wheel wear mainly take on-machine measurement systems or black-box algorithms. The first method has poor real-time performance for it can only be implemented after machining, making it difficult to adjust grinding processes in time. The second method has not revealed the mechanism of wear, resulting in poor model accuracy and flexibility. Besides, previous models are mainly for cylindrical wheels in surface grinding, while the properties of non-cylindrical wheel wear for gear grinding are different. To address this issue, a novel online monitoring method of non-cylindrical wheel wear for gear grinding is proposed. Firstly, a new parameter is defined based on grain height variation to describe the non-cylindrical wheel wear condition, avoiding the problem of underestimating the actual results due to a small measurement range. Then, a theoretical model for dynamic grinding forces is established considering more grain parameters and more complicated and realistic shape, so that on the basis of that model, the online monitoring method for wheel wear is proposed. Finally, experiments under different grinding conditions are conducted to verify the proposed models. The average calculation error of the grinding force model in different grinding parameters is 6.67%, and the maximum prediction error of the monitoring method under different wheel wear conditions is 8.2%.

Shot peening simulation oriented to residual stress interaction with gear grinding

The current study proposes a shot peening model which enables the residual stress interaction with grinding, a typical combination for gear finishing. The effect of the interaction on the stress state development was addressed by comparing the residual stress state from a standalone shot peening procedure, against the residual stress state arising from a manufacturing route where the interaction of shot peening and grinding takes place. In the interaction model, the grinding procedure generates a pre-loaded condition on the material, modifying the internal strain system of the gear tooth. This pre-loaded system, when disturbed by shot peening, reaches a new internal strain equilibrium. In the interaction model, a 24% less compressive stress state was attained when compared with the standalone shot peening process. A significant shift in the depth and magnitude of the peak compressive stress was also observed. On account of the numerical study of the processes’ interaction, the developed model substantially contributed to understanding the residual stress formation during manufacturing chains.